Reporting on facts relative to a specified dimensional coordinate constraint

ABSTRACT

A received report query specifies a dimension coordinate constraint and an associated grain for the dimension coordinate constraint. At least one query is generated to the dimensionally-modeled fact collection. A result of providing the at least one query to the dimensionally-modeled fact collection is processed. The processed result includes an indication of every dimension coordinate satisfying the dimension coordinate constraint and having a particular value at the associated grain, and the processed result further includes facts of the dimensionally-modeled fact collection that are specified by at least one other dimension coordinate indicated by the processed result having the particular value at the associated grain and not satisfying the dimension coordinate constraint.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior, co-pending U.S. patentapplication Ser. No. 11/552,394, filed Oct. 24, 2006, which isincorporated herein by reference in its entirety for all purposes.

BACKGROUND

The present invention is in the field of reporting on facts of orderived from a collection of facts organized as, or otherwise accessibleaccording to, a dimensional data model. For shorthand throughout thisdescription, such a collection of facts is referred to as adimensionally-modeled fact collection. In particular, the presentinvention relates to reporting on facts considering the phenomena inwhich a specific grain value of dimension coordinates satisfying a (oneor more) dimension coordinate constraint of a report query to adimensionally-modeled fact collection may also be the value, at thatgrain, for other dimension coordinates that do not satisfy the dimensioncoordinate constraint.

It is known to respond to a query to a dimensionally-modeled factcollection by reporting on the facts contained in thedimensionally-modeled fact collection. Reports are typically generatedto allow one to glean information from facts that are associated withlocations in a dimensional data space according to which thedimensionally-modeled fact collection is modeled.

Locations in an n-dimensional data space are specified by n-tuples ofcoordinates, where each member of the n-tuple corresponds to one of then dimensions. For example, (“San Francisco”, “Sep. 30, 2002”) mayspecify a location in a two-dimensional data space, where the dimensionsare LOCATION and TIME. Coordinates need not be single-grained entities.That is, coordinates of a single dimension may exist at, or be specifiedwith respect to, various possible grains (levels of detail). In oneexample, a coordinate of a LOCATION dimension comprises the followinggrains: CONTINENT, COUNTRY, CITY.

The order of the grains may have some hierarchical significance. Thegrains are generally ordered such that finer grains are hierarchically“nested” inside coarser grains. Using the LOCATION dimension example,the CITY grain may be finer than the COUNTRY grain, and the COUNTRYgrain may be finer than the CONTINENT grain. Where the order of thegrains of a dimension has hierarchical significance, the value of acoordinate of that dimension, at a particular grain, is nominally suchthat the value of the coordinate of that dimension has only one value atany coarser grain for that dimension. In an example, a value of acoordinate of a LOCATION dimension may be specified at the CITY grain ofthe LOCATION dimension by the value “Los Angeles.” This same coordinatehas only one value at the COUNTRY and CONTINENT grains: “US” and “NORTHAMERICA”, respectively.

SUMMARY

A method/system considers the phenomenon in which, for each of at leastone of the dimension coordinates that satisfy a dimension coordinateconstraint of a report query, that dimension coordinate has a particularvalue at a grain associated with the report query, and there are otherdimension coordinates that have that particular value at the associatedgrain and that do not satisfy the dimension coordinate constraint. Thisphenomenon may occur as a result of slowly changing dimensions and otherscenarios. The phenomenon is considered in the process of reporting onfacts of a collection of facts organized as, or otherwise accessibleaccording to, a dimensionally-modeled fact collection.

A received report query specifies a dimension coordinate constraint andan associated grain for the dimension coordinate constraint. At leastone query is generated to the dimensionally-modeled fact collection. Aresult of providing the at least one query to the dimensionally-modeledfact collection is processed. The processed result includes anindication of every dimension coordinate satisfying the dimensioncoordinate constraint. The processed result further includes facts ofthe dimensionally-modeled fact collection that are specified by theevery dimension coordinate satisfying the dimension coordinateconstraint, as well as facts of the dimensionally-modeled factcollection that are specified by at least one other dimension coordinatethat does not satisfy the dimension coordinate constraint, wherein eachof the at least one other dimension coordinate has a value at theassociated grain that is the same as the value at the associated grainof one of the dimension coordinates that does satisfy the dimensioncoordinate constraint.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example architecture of asystem in which reporting of facts of a dimensionally-modeled factcollection is performed in an unfettered manner.

FIG. 2 is a flowchart illustrating a multiple-pass processing method.

FIGS. 3A and 3B together illustrate an example of generating a report inan unfettered manner.

DETAILED DESCRIPTION

The inventors have realized that it is desirable to consider thephenomenon in which, for each of at least one of the dimensioncoordinates that satisfy a dimension coordinate constraint of a reportquery, that dimension coordinate has a particular value at a grainassociated with the report query and there are other dimensioncoordinates that have that particular value at the associated grain andthat do not satisfy the dimension coordinate constraint. This phenomenonarises when one or more dimensions in which the dimension coordinatesexist is a slowly changing dimension. In addition, there are otherscenarios in which, for each of one or more of the dimension coordinatessatisfying the report query, that dimension coordinate has a particularvalue at a grain associated with the report query and there are otherdimension coordinates that have that particular value at the associatedgrain and that do not satisfy the dimension coordinate constraint.

We first discuss the well-known phenomenon in the field of dimensionaldata modeling of “slowly changing dimensions.” This is a phenomenon inwhich the relationship of grains for a dimension may change over time.While it may be contrived to consider the concept of slowly changingdimensions with reference to the example LOCATION dimension (since,generally, the relationship of CONTINENT, COUNTRY and CITY grains willnot change over time), there are other more realistic examples of thisphenomenon.

As one illustration, consider an EMPLOYEE dimension that is intended torepresent an organizational chart of a company. In this example, theEMPLOYEE dimension comprises the following grains: ORGANIZATION,DIVISION, TEAM and PERSON. Using this example, it can be seen thatvalues of coordinates at various grains may change as a person movesfrom one team to another team (or, perhaps, a team moves from onedivision to another division). For example, in one month, Joe worked onthe Red Team; the next month, he worked on the Blue Team. This may bemodeled by one EMPLOYEE dimension coordinate having the value “Joe” atgrain PERSON and the value “Red Team” at grain TEAM, plus a secondEMPLOYEE dimension coordinate also having the value “Joe” and grainPERSON but the value “Blue Team” at grain TEAM. It is also possible toencode in the representation of the dimension coordinates the specifictime intervals during which these grain relationships obtained.

As more background to the issue of temporal dimensions (slowly changingdimensions), we use an example relative to the EMPLOYEE dimension,mentioned above, intended to represent an organizational chart of acompany. The EMPLOYEE dimension comprises the grains of ORGANIZATION,DIVISION, TEAM and PERSON. In this example, various people have movedonto and off of the Red Team. This phenomenon is modeled by EMPLOYEEdimension coordinates having value “Red Team” at grain TEAM, and havingvalues indicative of those people at grain PERSON; these dimensionmembers and that grain relationship are further qualified by the timesduring which that relationship obtained. These dimension members may berepresented as follows:

TABLE 1 date range for which this value at value at value at PERSONgrain has the value of PERSON grain TEAM grain “Red Team” at the TEAMgrain Joe Red Team 4 Jan. 2004 to 1 Mar. 2006 Mary Red Team 3 Mar. 2003to 18 Jul. 2006 Bill Red Team 1 Dec. 2005 to 12 Dec. 2005Continuing with the example, and noticing that Bill is only on the RedTeam for part of December 2005, consider that Bill was on the Blue Teamfrom 13 Dec. 2005 to 31 Dec. 2005. Finally, also consider that thenumber of calls taken during December 2005 by Joe, Mary and Bill (andZoe as well) is represented as follows:

TABLE 2 value at value at date range (during PERSON TEAM December) fornumber of grain grain “number of calls” value calls taken Joe Red Team 1Dec. 2005 to 10 31 Dec. 2005 Mary Red Team 1 Dec. 2005 to 22 31 Dec.2005 Bill Red Team 1 Dec. 2005 to 8 12 Dec. 2005 Bill Blue Team 13 Dec.2005 to 6 31 Dec. 2005 Zoe Blue Team 1 Dec. 2005 to 12 31 Dec. 2005

Given the preceding information, a report query may request the numberof calls taken during December 2005 by persons who were on the Red Teamsometime during December 2005. There are some analytic scenarios inwhich a user may desire to know the number of calls taken by each personon the Red Team (including Bill) during December 2005 to include onlythose calls taken while that person was actually on the Red Team (adimension coordinate constraint). These may be thought of as reportingon facts in a “fettered” manner, since the facts accounted for in thereported results not only correspond to the dimension coordinateconstraint of persons on the Red Team but, in addition, are bound by thedimension coordinate constraint of persons on the Red Team (i.e.,include facts for persons on the Red Team only while those persons areactually on the Red Team). Thus, for example, the record indicating thesix calls taken by Bill while Bill was on the Blue team is not utilizedfor the report.

This is what would be reported conventionally. For example, using thepreceding information regarding calls taken, such a report (i.e., withBill being attributed only those calls taken during December 2005 byBill while Bill is actually on the Red Team) may be as follows:

TABLE 3 value at PERSON grain December 2005 Joe 10 Mary 22 Bill 8

For example, using the preceding information regarding calls taken byJoe, Mary and Bill, it may be useful for a user to know the number ofcalls taken by each person on the Red Team during December 2005, withoutregard for whether that person was actually on the Red Team when thecalls were taken. That is, it may be useful for the facts accounted forin the reported results, while corresponding in some sense to thedimension coordinate constraint of persons on the Red Team, to be notbound by the dimension coordinate constraint of persons on the Red Team(i.e., may include facts for persons on the Red Team even while thosepersons were not actually on the Red Team). Thus, for example, therecord indicating the six calls taken by Bill while Bill was on the Blueteam is utilized for this unfettered report.

Reporting on the facts in such an unfettered manner, Bill is attributedcalls taken during December 2005 without regard for the team of whichBill is a member when a particular call is taken, and the report wouldbe as follows:

TABLE 4 value at PERSON grain December 2005 Joe 10 Mary 22 Bill 14

Thus far, we have discussed scenarios arising due to the phenomenon ofslowly changing dimensions. As mentioned above, the inventors haverealized that there are other analytic scenarios in which it would beuseful for the facts to be reported in an “unfettered manner” (i.e., ina manner that corresponds to, but is not strictly bound by the dimensioncoordinate constraint of the report query). In general, such analyticscenarios arise in situations where a grain value of dimensioncoordinates satisfying the dimension coordinate constraint of a reportquery may also be the value, at that grain, for other dimensioncoordinates that do not satisfy the dimension coordinate constraint ofthe report query.

For example, referring to Table 5 below, a dimension coordinateconstraint of a report query may be with respect to weeks in the monthof January (ignoring the beginning of January for simplicity ofexplanation).

TABLE 5 January week 3 10 January week 4 22 January week 5 8 Februaryweek 5 6 February week 6 3Thus, with reference to Table 5, a report query for calls taken during aweek in January (the dimension coordinate constraint being, in informalterms, “a week in January”) would result in the dimension coordinatescharacterized by:

-   -   “January” at the month grain and “week 3” at the week grain;    -   “January” at the month grain and “week 4” at the week grain; and    -   “January” at the month grain and “week 5” at the week grain.        However, the value of “week 5” at the week grain is also the        value, at the week grain, of the dimension coordinate        characterized by:    -   “February” at the month grain and “week 5” at the week grain.

Therefore, when reporting in a fettered manner, the reported “number ofcalls” in response to the report query of “calls taken during a week inJanuary” would result in a report of 10+22+8=40 calls. On the otherhand, when reporting in an unfettered manner, the reported “number ofcalls” in response to the same report query would result in a report of10+22+8+6=46 calls. That is, when reporting in a fettered manner, thesix calls occurring during week 5 in February would not be reported,since the portion of week 5 that is in February is not in January. Onthe other hand, when reporting in an unfettered manner, the six callsoccurring during week 5 in February would be reported. That is, thesecalls are still for a dimension coordinate with “week 5” at the weekgrain, even though the dimension coordinate does not satisfy the reportquery, since the dimension coordinate has the value “February” at themonth grain.

FIG. 1 is a block diagram illustrating an example architecture of asystem 100 in which reporting of facts of a dimensionally-modeled factcollection is performed in an unfettered manner. Referring to FIG. 1, auser 102 may cause a report query 104 to be provided to a factcollection query generator 106. For example, the user 102 may interactwith a web page via a web browser, where the web page is served by areport user interface using, for example, a Java Server Page mechanism.In this example, the user 102 interacts with the web page such that thereport query 104 is provided to the fact collection query generator 106.The report query 104 includes a dimension coordinate constraint, whichmay be one or more dimension coordinate constraints.

In general, a dimension coordinate constraint for a dimension of thedimensionally-modeled fact collection specifies coordinates of thatdimension of the dimensionally-modeled fact collection. For example, adimension coordinate constraint may specify coordinates of thatdimension of the dimensionally-modeled fact collection by specifying avalue of the dimension at a particular grain. Dimension coordinateconstraints of the report query 104, then, specify a subset ofcoordinates of one or more dimensions of the dimensionally-modeled factcollection, on which it is desired to report.

The fact collection query generator 106 processes the report query 104to generate an appropriate corresponding fact collection query 108,which is presented to the dimensionally-modeled fact collection 110. Aresult 116 of presenting the fact collection query 108 to thedimensionally-modeled fact collection 110 is processed by a reportgenerator 118 to generate a report corresponding to the report query 104caused to be provided by the user 102. In particular, the generatedreport includes an indication of dimensional members as appropriate inview of the dimensional coordinate constraints of the report query 104.

In one example, the dimensionally-modeled fact collection 110 isimplemented as a relational database, storing fact data in a manner thatis accessible to users according to a ROLAP—Relational Online AnalyticalProcessing—schema (fact and dimension tables). In this case, the factcollection query 108 may originate as a database query, in some formthat is processed into another form, for example, which is processed byan OLAP query engine into a fact collection query 108, presented as anSQL query that is understandable by the underlying relational database.This is just one example, however, and there are many other ways ofrepresenting and accessing a dimensionally-modeled fact collection.

Processing 118 is applied to the fact collection result 116 to generatea report. The generated report includes an indication of dimensionmembers and facts corresponding to those indicated dimension members.For example, the facts corresponding to those indicated dimensionmembers may be reported in an “unfettered” (i.e., in a manner that isnot bound by the dimension coordinate constraint of the report query)such as is discussed above relative to the example of Bill and the RedTeam. The facts corresponding to those indicated dimension members maybe reported in a “fettered” manner (i.e., in a manner that is bound bythe dimension coordinate constraints of the report query 104).

Referring still to FIG. 1, the composition of the generated report maybe accomplished by the fact collection query generator 106 particularlygenerating the fact collection query 108 in accordance with the reportquery, by the result processing 118 particularly processing the factcollection result (e.g., by applying filtering) in accordance withreport query, or by a combination of both.

As also illustrated in FIG. 1, the report query 104 may include anunfettered/fettered mode designation, which may be provided, forexample, via a user interface. In some examples, in the absence of suchan unfettered/fettered mode designation, the manner in which the factscorresponding to those indicated dimension members are reported may beaccording to a default mode or according to a preconfigured mode. Thefact collection query generator 106 and/or the result processing 118, asappropriate, operate according to the default, preconfigured ordesignated mode.

In accordance with one example, a multiple-pass processing is utilized.An example of the multiple-pass processing is illustrated by theflowchart of FIG. 2. In a first pass 202, every dimension coordinate(which may be one or more dimension coordinates) that satisfies thedimension coordinate constraint is determined. The determined dimensioncoordinates have a particular value at a particular grain. In anotherpass 204, at least one other dimension coordinate (which may be one ormore other dimension coordinates) is determined that does not satisfythe dimension coordinates constraint, wherein each of the at least oneother dimension coordinates has a value at the associated grain that isthe same as the value at the associated grain of one of the dimensioncoordinates determined in the first pass 202. In yet another pass 206,the facts of the dimensionally-modeled fact collection are determinedthat are specified by the determined dimension coordinates and by thedetermined at least one other dimension coordinate.

FIG. 3A illustrates steps of a specific example of generating a reportin an unfettered manner. Corresponding FIG. 3B provides supplementalexplanatory material for the FIG. 3A example. Referring to FIG. 3A, atstep 302, a report query is received, and the report query includes adimension coordinate constraint of “persons on the Blue Team.” Oval 352of FIG. 3B illustrates a formalistic representation of the dimensioncoordinate constraint and the associated grain. At step 304 of FIG. 3A,the received report query is processed to obtain all (one or more)dimension coordinates in which the value at the team grain is “BlueTeam.” Oval 354 of FIG. 3B indicates that, in the example, the obtaineddimension coordinates have a value of “Joe,” “Sally” or “Mista” at thePerson grain (in addition to having the value of “Blue Team” at the Teamgrain).

At step 306, all (one or more) dimension coordinates are obtained thatsatisfy a replacement dimension coordinate constraint, including thegrain values obtained at step 304 (“Joe,” “Sally” or “Mista”), withoutthe constraint of “person on the Blue Team.” Oval 356 illustrates aformalistic representation of the step 306 dimension coordinateconstraint.

At step 308, operations corresponding to the report query are performedon the facts specified by the dimension coordinates obtained at step306. That is, as illustrated by oval 358, operations to generate thereport are performed on the facts specified by all dimension coordinatesfor which the value at the Person grain is “Joe,” “Sally” or “Mista” butfor which the value at the Team grain may be, but is not necessarily,“Blue Team.”

We have described herein a method/system which considers the phenomenonin which a particular value at a grain associated with a dimensioncoordinate constraint of a report query may also be the value, at theassociated grain, for other dimension coordinates that do not satisfythe dimension coordinate constraint of the report query.

1. A method of processing a request for information associated withdimensionally modeled organizational information in which an employeedimension includes a set of grains having a relationship with eachother, the request including a specification of a dimension coordinateconstraint and a specification of a particular associated grain for thedimension coordinate constraint, the method comprising: receiving thespecification of a dimension coordinate constraint that is a teamconstraint and the specification of a particular associated grain forthe team constraint corresponding to a person grain; processing anindication of a fact reporting mode, the fact reporting mode beingselectable by a user designation; generating at least one query to adimensionally-modeled fact collection; and processing a result ofproviding the at least one query to the dimensionally-modeled factcollection, wherein the processed result includes an indication of everydimension coordinate satisfying the team constraint, and wherein theprocessed result further includes: in response to the indication of thefact reporting mode being the fettered mode, reporting fettered facts ofthe dimensionally-modeled fact collection, the fettered facts includingan attribution for every person in a person dimension coordinatesatisfying the team constraint during a time period of interest, and inresponse to the indication of the fact reporting mode indicating anunfettered mode, automatically reporting unfettered facts of thedimensionally-modeled fact collection, the unfettered facts beingspecified by determining, for every person in the person dimensioncoordinate satisfying the team constraint, an attribution unbounded bythe team constraint during the time period of interest; and returningthe processed result in response to the request; the unfettered factsproviding an additional type of selectable attribution information forthe returned processed result that is not strictly bound by the teamconstraint to account for a situation in which one or more employeestransition between teams over the time period of interest.
 2. The methodof claim 1, wherein the attribution for each person in the persondimension satisfying the dimension coordinate constraint corresponds tothe number of contacts handled within the time period of interest. 3.The method of claim 1, wherein the unfettered facts are generated in asecond pass of processing by utilizing the indication of every person inthe person dimension coordinate satisfying the team constraint and thenanalyzing, for each person in the set, the cumulative attribution ofeach person in the time period of interest regardless of teammembership.
 4. The method of claim 1, wherein returning the processedresult comprises: reporting the processed result, wherein the reportincludes an indication of each person that satisfies the team constraintin the time period of interest and further includes facts for eachperson on the team in the time period of interest indicating theattribution of each person on the team.
 5. The method of claim 4,wherein for the unfettered mode the facts are reported in an unfetteredmanner in which for a person on the team that also belonged to at leastone other team during the time period of interest the unfettered factsincludes for that person the cumulative attribution for all teams thatthe person was a member of during the time period of interest.
 6. Themethod of claim 1, wherein the attribution for each person in the persondimension satisfying the dimension coordinate constraint corresponds tothe number of calls handled within the time period of interest.
 7. Themethod of claim 1, further comprising: receiving the indication of thefact reporting mode via a user interface.
 8. A method of attributingcredit for contacts handled within an organization having dimensionallymodeled employee organizational information including dimensioncoordinates and associated grains by processing a request including aspecification of a dimension coordinate constraint defining a team inthe organization chart and a specification of a person grain for theteam constraint, to a dimensionally-modeled fact collection, the methodcomprising: receiving a specification of the team constraint and aspecification of a person grain for the team constraint; processing anindication of a fact reporting mode, the fact reporting mode beingselectable by a user designation; generating at least one query to thedimensionally-modeled fact collection; and processing a result ofproviding the at least one query to the dimensionally-modeled factcollection and returning a report, wherein the report includes anindication of every person in a person dimension coordinate satisfyingthe team constraint, and wherein the processed result further includes:fettered facts of the dimensionally-modeled fact collection, thefettered facts being specified for every person in a person dimensioncoordinate satisfying the team constraint while a member of the teamduring a time period of interest to attribute the number ofcommunication contacts handled by each member of the time while they aremembers of a team satisfying the team constraint during the time periodof interest, and in response to the indication of the fact reportingmode indicating an unfettered mode, automatically including unfetteredfacts of the dimensionally-modeled fact collection, the unfettered factsbeing specified by determining, for every person in the person dimensioncoordinate satisfying the team constraint, facts unbounded by the teamconstraint to attribute the total number of communication contactshandled by each person that is a member of the team for any portion ofthe time period of interest; and returning the processed result inresponse to the request; the unfettered facts providing an additionaltype of selectable information for the returned processed result that isnot strictly bound by the team constraint to account for a situation inwhich one or more individual persons transition between teams over thetime period of interest.
 9. The method of claim 8, wherein theunfettered facts are generated in a second pass of processing byutilizing the indication of every person in the person dimensioncoordinate satisfying the team constraint and then analyzing, for eachperson in the set, the cumulative attribution of each person in the timeperiod of interest regardless of team membership.
 10. The method ofclaim 8, further comprising: receiving the indication of the factreporting mode via a user interface.
 11. A method of processing arequest for information associated with an organizational chart havingan employee dimension and associated grains by providing a requestincluding a specification of a dimension coordinate constraint that is ateam constraint and a specification of a particular associated graincorresponding to a person grain, to a dimensionally-modeled factcollection, the method comprising: receiving the specification of theteam constraint and the specification of the person grain for the teamconstraint; processing an indication of a fact reporting mode selectableby a user designation, wherein the fact reporting mode is one of afettered mode and an unfettered mode; generating at least one query tothe dimensionally-modeled fact collection; processing a result ofproviding the at least one query to the dimensionally-modeled factcollection, and processing the indicated fact reporting mode; wherein ina first pass of processing the processed result includes: an indicationof every dimension member at the person level satisfying the teamconstraint, wherein for the fettered mode facts reporting factsattributed to each dimension member that is a member of a teamsatisfying the team constraint during a time period of interest; andwherein for the unfettered mode, performing a second pass of processingfor each dimension member determined in the first pass of processing,the second pass of processing and reporting facts attributed to eachrespective dimension member unbounded by the team constraint; theunfettered facts providing an additional type of selectable informationfor the returned processed result that is not strictly bound by the teamconstraint to account for a situation in which at least one person isrelated to more than one team over a time period of interest.
 12. Themethod of claim 11, wherein the facts include the number of contactshandled during the time period of interest.
 13. The method of claim 12,wherein in the fettered mode a report is generated providing a listingof the persons belonging to the team during the time period of interestand the number of contacts handled by each person only while they are amember of the team.
 14. The method of claim 13, wherein the unfetteredmode a report is generated providing a listing of the persons belongingto the team during the time period of interest and the cumulative numberof contacts handled by each person including any calls handled while onanother team for a portion of the time period of interest.
 15. Themethod of claim 11, wherein the facts include the number of callshandled during the time period of interest.
 16. A method of processing arequest for information to attribute factual information handled withinan organization having dimensionally modeled organizational informationin which an employee dimension includes a set of grains having arelationship with each other, the request including a specification of adimension coordinate constraint and a specification of a particularassociated grain for the dimension coordinate constraint, to adimensionally-modeled fact collection, the method comprising: receivingthe specification of a dimension coordinate constraint and thespecification of an associated grain; processing an indication of a factreporting mode, the fact reporting mode being selectable by a userdesignation; generating at least one query to the dimensionally-modeledfact collection; and processing a result of providing the at least onequery to the dimensionally-modeled fact collection, wherein theprocessed result includes an indication of dimension coordinatesatisfying the team constraint, and wherein the processed result furtherincludes: fettered facts of the dimensionally-modeled fact collection,the fettered facts including a fact attribution for every dimension inthe dimension coordinate constraint satisfying the dimension coordinateconstraint during a time period of interest, and in response to theindication of the fact reporting mode indicating an unfettered mode,automatically including unfettered facts of the dimensionally-modeledfact collection, the unfettered fact attribution being specified bydetermining for every dimension value satisfying the dimensioncoordinate constraint, attribution information unbounded by thedimension coordinate constraint during the time period of interest; andreturning the processed result in response to the request; theunfettered facts providing an additional type of selectable attributionfor the returned processed result that is not strictly bound by thedimension coordinate constraint to account for a situation in which thegrain relationships change during the time period of interest.
 17. Themethod of claim 16, wherein the employee dimension includes at least adivision grain, a team grain, and a person grain further wherein thedimension coordinate constraint is a division constraint and at leastone team moves from one division to another during the time period ofinterest.
 18. The method of claim 16, wherein the fact attributionincludes a number of contacts handled during the time period ofinterest.
 19. The method of claim 16, wherein fact attribution includesa number of calls handled during the time period of interest.
 20. Themethod of claim 16, wherein the employee dimension includes at least ateam grain and a person grain, further wherein the dimension coordinateconstraint is a team constraint and at least one individual persontransitions between teams during the time period of interest.
 21. Themethod of claim 20, wherein the fact attribution includes a number ofcontacts handled during the time period of interest.
 22. The method ofclaim 20, wherein fact attribution includes a number of calls handledduring the time period of interest.